Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin

High precision hybrid RF and ultrasonic chirp-based ranging for low-power IoT nodes

Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 µW for a single measurement is achieved. The operation time is estimated on 8.5 years on a CR2032 coin cell battery at a 1 Hz update rate, which is over 250 times larger than state-of-the-art RF-based positioning systems. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-noise ratios and when reflections occur. We introduce three methods that enhance the distance measurements at a low extra processing power cost. Hence, we validate in realistic environments that the centimeter accuracy can be obtained within the energy budget of mobile devices and IoT nodes. The proposed hybrid signal ranging system can be extended to perform accurate, low-power indoor positioning

Cross-layer framework and optimization for efficient use of the energy budget of IoT Nodes

Both physical and MAC-layer need to be jointly optimized to maximize the autonomy of IoT devices. Therefore, a cross-layer design is imperative to effectively realize Low Power Wide Area networks (LPWANs). In the present paper, a cross-layer assessment framework including power modeling is proposed. Through this simulation framework, the energy consumption of IoT devices, currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a cross-layer approach significantly improves energy efficiency and overall throughput. Two major contributions are made. First, an open-source LPWAN assessment framework has been conceived. It allows testing and evaluating hypotheses and schemes. Secondly, as a representative case, the LoRaWAN protocol is assessed. The findings indicate how a cross-layer approach can optimize LPWANs in terms of energy efficiency and throughput. For instance, it is shown that the use of larger payloads can reduce up to three times the energy consumption on quasi-static channels yet may bring an energy penalty under adverse dynamic conditions

Direct electrodeposition of aluminium nano-rods

Electrodeposition of aluminium within an alumina nano-structured template, for use as high surface area current collectors in Li-ion microbatteries, was investigated. The aluminium electrodeposition was carried out in the ionic liquid 1-ethyl-3-methylimidazolium chloride:aluminium chloride (1:2 ratio). First the aluminium electrodeposition process was confirmed by combined cyclic voltammetry and electrochemical quartz crystal microbalance measurements. Then, aluminium was electrodeposited under pulsed-potential conditions within ordered alumina membranes. A careful removal of the alumina template unveiled free standing arrays of aluminium nano-rods. The nano-columns shape and dimensions are directly related to the template dimensions. To our knowledge, this is the first time that direct electrodeposition of aluminium nano-pillars onto an aluminium substrate is reported

Integrated risk assessment of selected mycotoxins in fresh produce and derived food products throughout the food chain, affected by climate changes and globalization

Fruits and vegetables are an important part of a healthy diet, and their consumption is expected to increase in the future because of health promotion. However, climate change and globalization will have an effect on their food safety (Paterson & Lima 2010). In order to maintain the desired level of food safety in Europe, it is necessary to explore new food contamination pathways and approaches to deal with these projected changes. An imported food safety problem is the presence of fungi and mycotoxins. (Semi) dried plants are mainly associated with mycotoxins but recently fresh produce are associated with new emerging mycotoxins. The objective of the research is to develop a farm-to-fork risk assessment model to predict the mycotoxin concentration in fresh and derived products in order to predict future risks due to climate change and growing import of foods from third countries. An initial inventory is made of relevant moulds and mycotoxins present on fresh produce and derived food products. Therefore data of mycotoxin concentration on dried plant, fresh and derived products are collected. This is done in cooperation with ICPC partners (e.g. Egypt, Brazil, Serbia and India) and is extended with European and national data. The data are obtained by including both scientific articles and grey literature (e.g. EFSA, RASFF). Most data are found from dried products, such as nuts, dried fruits and spices and herbs. Almost no data is available on fresh produce. To collect additional information (on fresh produce and derived products) a screening method with LC-TOF-MS is running for ochratoxin A, fumonisin B1, B2, B3, alternariol and alternariol monomethyl ether in tomatoes, onions, sweet bell peppers and soft red fruits. The MS parameters were tuned for each mycotoxin and both positive and negative electrospray conditions were checked. It was decided to screen for the mycotoxins in two separated runs (positive and negative electrospray run). The six mycotoxins can be screened in one sample in a relative short time of one hour. To screen for patulin we performed an non quantitative method with an HPLC with an extraction method described by Sanzani et al. (Sanzani et al. 2009). Preliminary results showed a presence of 14% of patulin in mouldy tomatoes (15 out of 107)

Choix d'un modèle de pyrolyse ménagée du bois à l'échelle de la microparticule en vue de la modélisation macroscopique

Par définition la pyrolyse ménagée du bois est la décomposition physique et chimique de matières organiques sous l'action de la chaleur et en absence d'oxygène. Comprendre ce phénomène passe d'abord par l'identification des mécanismes réactionnels et la détermination des paramètres cinétiques mis en jeu lors de la dégradation thermique du bois et de ses constituants majeurs, c'est-à-dire cellulose, hémicelluloses et lignines. La richesse et la diversité des résultats issus de la littérature spécialisée rendent compte de la difficulté à expliquer ces cinétiques complexes. Cette étude se propose de contribuer de façon innovante au bois traité à haute température en proposant une approche théorique à l'échelle de la microparticule en vue d'une modélisation macroscopique des phénomènes couplés « thermiques, chimiques et physiques ». En restituant les résultats d'une étude bibliographique poussée, nous avons fait le choix de retenir une approche analytique cherchant à séparer les trois composés principaux du bois et à caractériser chacun d'entre eux séparément

Determination of local material properties of OSB sample by coupling advanced imaging techniques and morphology-based FEM simulation

This is the publisher’s final pdf. The published article is copyrighted by Walter de Gruyter & Co. and can be found at: http://www.degruyter.com/.The goal was to determine local mechanical properties inside of oriented strand board (OSB) based on a realistic morphology-based finite element (FE) model and data acquired from a physical test performed on the same material. The spatial information and local grayscale intensity from CT-scans obtained from small OSB sample was transformed into a 2D regular morphology-based FE mesh with corresponding material properties. The model was then used to simulate the actual compression test performed on the specimen using simplified boundary conditions. The simulated strain fields from the model were compared with the actual strain field measured on the specimen surface during the compression test by means of a full-field optical method, named digital image correlation (DIC). Finally, the original set of material properties was adjusted by an iterative procedure to minimize the difference between the simulated and the measured strain data. The results show that the developed procedure is useful to find local material properties as well as for morphological modeling without the need of segmentation of the image data. The achieved results serve as a prerequisite for full 3D analyses of the complex materials

A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks

Massive MIMO is a promising technology to connect very large numbers of energy constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes in groups that can communicate simultaneously such that the mutual interference is minimized. We here propose node partitioning strategies that do not require full channel state information, but rather are based on nodes' respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency